Many methods and systems for providing information, ratings and/or recommendations on text or written materials, such as books, involve computer-based environments and access to databases storing information on such written materials. In many such networked computer environments, the requirements for providing useful information, ratings or recommendations can vary greatly in response to input from or information regarding the subject user (or “customer” hereinafter). For example, in situations where a customer desires book information or recommendations via the World Wide Web (i.e., a book shopping search, such as on Amazon.com®), knowledge of parameters such as what types of books the customer enjoys as well as which books the customer has already read are required to provide the most useful results. Not only do these parameters vary greatly from customer to customer, but they can also include information as to how the tastes of one customer correspond to those of another. One method of generating such personalized recommendation is known as collaborative filtering.
Collaborative filtering methods in this field operate at the most basic level by asking each customer to rate books or other written matter that the customer is familiar with. These ratings are then analyzed and used to correlate and divulge various specific characteristics or commonalities from the totality of ratings data. A profile may be derived for each customer, and comparison of one customer's profile with similar profiles can be done to identify items of potential interest.
Regardless of the specific parameters or filtering method involved, however, the information, ratings and/or recommendations must be useful to the customer and helpful for the task at hand to ensure continued popularity and success of that network environment. For example, current methods of providing book ratings or recommendations are frequently unsatisfactory due to the lack of information provided, lack of more appropriate information, and/or the inefficient or otherwise problematic functionality of acquiring information. Particularly with regard to the provision of book-related systems over networked computer environments, a customer is frequently given ratings or recommendations that are only based on information on what other people thought about the written material in general. However, for ratings or recommendations that require more useful and/or individual-specific response, current methods of book or text content related assistance and interaction have significant drawbacks.
One problem with current methods of providing such text content-related assistance over a network is that the procedures undertaken to determine the ratings or recommendations do not take into account information relating to the current customer's profile, likes and/or dislikes. For text content-related assistance that involves some analysis of the current customer's preferences to provide useful information for more helpful ratings and recommendations, this presents a burden in the provision of effective assistance.
A drawback with some current collaborative methods of providing text content-related assistance over a network is that the procedures undertaken to acquire and populate a database with current customer preference data frequently present a complex, large or frustrating burden to customers, particularly new users, of the system. For systems that otherwise derive advantage from ease-of-use and/or other such characteristics of attractiveness to a customer, this again presents a burden in acquiring appropriate results and presenting helpful information.
Another drawback is that current methods of such ratings or recommendations typically use static or overly simplistic algorithms for determining potential books for a customer. This approach often leads to non-dynamic results and misses the objective of obtaining information that is as useful as possible.
Therefore, current systems and methods of rating or recommendation are generally unable to provide the usefulness, flexibility, and customer-specific objectives required to efficiently and effectively provide the satisfactory results necessary for meaningful identification, rating or recommendation of text content, such as books.